{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c2df7b4b",
   "metadata": {},
   "source": [
    "# Propensity Score Matching with DID: Use Case and Examples\n",
    "\n",
    "Propensity score matching is a widely used causal inference analysis tool. As a data-preprocess tool, it can be combined with different estimation method such as inverse propensity score estimator, ordinary least square, DID (difference in difference), heterogeneuos treatment effect estimatior (HTE), etc. In this notebook, we'll demonstrate how to use the PSM to apply DID to synthetic data.\n",
    "\n",
    "### Data\n",
    "\n",
    "PSM in settings where we have several different types of observations:\n",
    "* Covariates, which we will denote with `X`\n",
    "* Treatment, which we will denote with `T`\n",
    "* Responses, which we will denote with `Y`\n",
    "\n",
    "Requirement is that `T` is a binary varible which contain only 0/1 values.\n",
    "\n",
    "### Estimation\n",
    "\n",
    "The PSM estimator uses a two-stage approach:\n",
    "1. It estimates the *propensity score* of the treatment `T` given `X`, using a CART model you input.\n",
    "2. It estimates the distance bewteen one obs and another using KNN and choose the nearest one.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ce4cf475",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "current version is:  0.0.5\n",
      "/usr/local/bin/python3.12\n",
      "3.12.2 (v3.12.2:6abddd9f6a, Feb  6 2024, 17:02:06) [Clang 13.0.0 (clang-1300.0.29.30)]\n",
      "sys.version_info(major=3, minor=12, micro=2, releaselevel='final', serial=0)\n"
     ]
    }
   ],
   "source": [
    "# %reload_ext autoreload\n",
    "# %autoreload 2\n",
    "# import sys\n",
    "# print(sys.path)\n",
    "# sys.path.append('..') \n",
    "\n",
    "# Import the most update version of causalmatch\n",
    "import causalmatch as causalmatch\n",
    "from causalmatch import matching, gen_test_data, gen_test_data_panel_with_selection, gen_test_data_panel\n",
    "\n",
    "print('current version is: ',causalmatch.__version__)\n",
    "\n",
    "# check which python jupyter notebook links to\n",
    "import sys\n",
    "print(sys.executable)\n",
    "print(sys.version)\n",
    "print(sys.version_info)\n",
    "\n",
    "# Import the rest of dependencies\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import recall_score,roc_auc_score,f1_score\n",
    "import statsmodels.api as sm\n",
    "from matplotlib import pyplot\n",
    "\n",
    "# to use fixedeffect model, set pandas version to 1.3.5\n",
    "from fixedeffect.fe import fixedeffect, did, getfe\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b06894b",
   "metadata": {},
   "source": [
    "## 1. Generate synthetic data\n",
    "\n",
    "To demonstrate the approach, we'll construct a synthetic dataset obeying the requirements set out above.  In this case, we'll take `X`, `T`, `y` to come from the following distribution: \n",
    "\n",
    "y_it = x_it * beta + treatment * ate + c_i + a_t + e_it\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dd919113",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For each id contains how many time periods observations\n",
      "id\n",
      "1         8\n",
      "10        8\n",
      "100       9\n",
      "1000      7\n",
      "10000     8\n",
      "         ..\n",
      "9995     10\n",
      "9996      9\n",
      "9997      9\n",
      "9998     10\n",
      "9999     10\n",
      "Name: time, Length: 50000, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "N = 50000\n",
    "T = 10\n",
    "beta = [-1, 0.2, 0.3, 0.5]\n",
    "ate  = [5]\n",
    "\n",
    "# 'exp_date': the date where treatment status for exp group swicth from 0 to 1\n",
    "exp_date = 6\n",
    "\n",
    "# 'unbalance':  not all id can observe all time periods value, ie. some obs can observe 10 days observation, some has only 1 obs.\n",
    "unbalance = True\n",
    "\n",
    "# df = gen_test_data_panel(N, T, beta, ate, exp_date, unbalance=True)\n",
    "\n",
    "# Added selection bias during sample generation\n",
    "df = gen_test_data_panel_with_selection(N, T, beta, ate, exp_date, unbalance=True)\n",
    "\n",
    "df['id'] = df['id'].astype('int').astype('str')\n",
    "\n",
    "\n",
    "# Add one more column of categorical covariates\n",
    "np.random.seed(123456)\n",
    "list_choice = list(range(5))\n",
    "array_prob = np.random.rand(5)\n",
    "list_prob = list(array_prob / np.sum(array_prob))\n",
    "\n",
    "rand_discrete = np.random.choice(a = list_choice, size = [df.shape[0],1],p = list_prob)\n",
    "df['x_discrete'] = rand_discrete.flatten()\n",
    "\n",
    "print('For each id contains how many time periods observations')\n",
    "print(df.groupby('id').time.nunique())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65955b88",
   "metadata": {},
   "source": [
    "## 2. Transform panel data to pre-treated cross-sectional data\n",
    "\n",
    "Use pre-treated period cross sectional data to do matching. You can use either t_treat-1 data or t=0~t=t_treat-1's average.You can also include lagged y variable.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f7b9fe35",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_base = pd.DataFrame()\n",
    "\n",
    "\n",
    "# 1. collect all ID\n",
    "df_base['id'] = list(df['id'].value_counts().index)\n",
    "\n",
    "\n",
    "# 2. collect all discrete variables, set each obs' value equals pre treatment's last period's\n",
    "df['rank'] = df.groupby(['id'])['time'].transform('rank', method='first')\n",
    "df_x_discrete = df[(df['time']<='date_04') & (df['rank']==1.0)][['id','x_discrete','rank']].copy()\n",
    "df_x_discrete['x_discrete'] = df_x_discrete['x_discrete'].astype(object)\n",
    "\n",
    "\n",
    "# 3. collect all id's treatment status\n",
    "df_treat_status = pd.DataFrame(df.groupby('id').treatment.sum().copy())\n",
    "df_treat_status.reset_index(inplace=True)\n",
    "df_treat_status['treatment_'] = df_treat_status['treatment'].apply(lambda x: 1 if x > 0 else x)\n",
    "\n",
    "\n",
    "# 4. collect all id's continuous variables, which equals pre-treated average\n",
    "df_x_continuous_extract = df[df['time']<='date_04'][['id','x_0', 'x_1', 'x_2', 'x_3']].copy()\n",
    "df_x_continuous = df_x_continuous_extract.groupby('id')[['x_0', 'x_1', 'x_2', 'x_3']].mean().copy()\n",
    "df_x_continuous.reset_index(inplace=True)\n",
    "\n",
    "\n",
    "# 5. merge data used for matching together\n",
    "df_base1 = df_base.merge(df_x_discrete[['id','x_discrete']], how='left', on='id')\n",
    "df_base2 = df_base1.merge(df_treat_status[['id','treatment_']], how='left', on='id')\n",
    "df_base3 = df_base2.merge(df_x_continuous[['id','x_0', 'x_1', 'x_2', 'x_3']], how='left', on='id')\n",
    "\n",
    "# 6. Because panel is imbalance, drop those obs with na values.\n",
    "df_base3.dropna(inplace=True)\n",
    "df_base3.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ca918c9",
   "metadata": {},
   "source": [
    "## 3. Match pre-treatment data using PSM\n",
    "\n",
    "Use pre-treated data to do matching. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8e5087e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Covariates  treat_control_diff_pre  treat_control_diff_post\n",
      "0           x_1                  0.4681                   0.0017\n",
      "1           x_2                  0.4015                   0.0022\n",
      "2           x_3                  0.3707                  -0.0061\n",
      "3  x_discrete_1                 -0.0011                  -0.0020\n",
      "4  x_discrete_2                 -0.0013                  -0.0015\n",
      "5  x_discrete_3                  0.0012                   0.0042\n",
      "6  x_discrete_4                  0.0006                  -0.0007\n"
     ]
    }
   ],
   "source": [
    "# STEP 1: matching\n",
    "match_obj = matching(data = df_base3,\n",
    "                     T = 'treatment_',\n",
    "                     X = ['x_1', 'x_2', 'x_3','x_discrete'],\n",
    "                     id = 'id')\n",
    "\n",
    "match_obj.psm(n_neighbors = 4, \n",
    "              model = LogisticRegression(), \n",
    "              trim_percentage = 0.01, \n",
    "              caliper = 1) \n",
    "\n",
    "res_post, res_pre = match_obj.balance_check(include_discrete = True)\n",
    "res_pre['treat_control_diff_pre']  = (res_pre['Mean Treated pre-match'] - res_pre['Mean Control pre-match'])\n",
    "res_pre['treat_control_diff_post'] = (res_post['Mean Treated post-match'] -res_post['Mean Control post-match'])\n",
    "\n",
    "# Check if imbalance becomes balance\n",
    "print(res_pre[['Covariates','treat_control_diff_pre','treat_control_diff_post']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1ccec55e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "bins = np.linspace(0, 1, 100)\n",
    "\n",
    "df_pre = match_obj.data_ps\n",
    "pyplot.hist(df_pre[df_pre['treatment_']==1]['pscore'].values, bins, alpha=0.5, label='x')\n",
    "pyplot.hist(df_pre[df_pre['treatment_']==0]['pscore'].values, bins, alpha=0.5, label='y')\n",
    "pyplot.legend(loc='upper right')\n",
    "pyplot.title('pre-match pscore distribution')\n",
    "pyplot.show()\n",
    "\n",
    "\n",
    "pyplot.hist(match_obj.df_out_final['pscore_treat'].values, bins, alpha=0.5, label='x')\n",
    "pyplot.hist(match_obj.df_out_final['pscore_control'].values, bins, alpha=0.5, label='y')\n",
    "pyplot.legend(loc='upper right')\n",
    "pyplot.title('post-match pscore distribution')\n",
    "pyplot.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f5080a7",
   "metadata": {},
   "source": [
    "## 4. DID\n",
    "\n",
    "Use DID to estimate post-match data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "80599d41",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dependent variable(s): ['y']\n",
      "independent(exogenous): []\n",
      "category variables(fixed effects): ['id', 'time']\n",
      "cluster variables: ['0']\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/fixedeffect/utils/GenCrossProd.py:55: UserWarning: You are doing DID with group effect where group is exp or base\n",
      "  warnings.warn('You are doing DID with group effect where group is exp or base')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    High Dimensional Fixed Effect Regression Results                   \n",
      "=======================================================================================\n",
      "  Dep. Variable:               y   R-squared(proj model):                      0.2519  \n",
      "  No. Observations:       417923   Adj. R-squared(proj model):                 0.1585  \n",
      "  DoF of residual:      371519.0   R-squared(full model):                      0.4434  \n",
      "  Residual std err:       3.3737   Adj. R-squared(full model):                 0.3739  \n",
      "  Covariance Type:     nonrobust   F-statistic(proj model):                 6.255e+04  \n",
      "  Cluster Method:     no_cluster   Prob (F-statistic (proj model)):                 0  \n",
      "                                   DoF of F-test (proj model):        [2.0, 371519.0]  \n",
      "                                   F-statistic(full model):                    6.3787  \n",
      "                                   Prob (F-statistic (full model)):                 0  \n",
      "                                   DoF of F-test (full model):        [46404, 371519]  \n",
      "=======================================================================================================\n",
      "                                  coef nonrobust std err          t      P>|t|     [0.025     0.975]   \n",
      "-------------------------------------------------------------------------------------------------------\n",
      "  const                       -1.24220           0.00763  -162.7406     0.0000    -1.2572    -1.2272   \n",
      "  treatment*post_experiment    4.10480           0.02282   179.8774     0.0000     4.0601     4.1495   \n",
      "  treatment                    0.31288           0.01909    16.3897     0.0000     0.2755     0.3503   \n",
      "=======================================================================================================\n",
      "dependent variable(s): ['y']\n",
      "independent(exogenous): []\n",
      "category variables(fixed effects): ['id', 'time']\n",
      "cluster variables: ['0']\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    High Dimensional Fixed Effect Regression Results                   \n",
      "=======================================================================================\n",
      "  Dep. Variable:               y   R-squared(proj model):                      0.2599  \n",
      "  No. Observations:       450001   Adj. R-squared(proj model):                 0.1673  \n",
      "  DoF of residual:      399990.0   R-squared(full model):                      0.4448  \n",
      "  Residual std err:       3.3163   Adj. R-squared(full model):                 0.3754  \n",
      "  Covariance Type:     nonrobust   F-statistic(proj model):                 7.022e+04  \n",
      "  Cluster Method:     no_cluster   Prob (F-statistic (proj model)):                 0  \n",
      "                                   DoF of F-test (proj model):        [2.0, 399990.0]  \n",
      "                                   F-statistic(full model):                    6.4077  \n",
      "                                   Prob (F-statistic (full model)):                 0  \n",
      "                                   DoF of F-test (full model):        [50011, 399990]  \n",
      "=======================================================================================================\n",
      "                                  coef nonrobust std err          t      P>|t|     [0.025     0.975]   \n",
      "-------------------------------------------------------------------------------------------------------\n",
      "  const                       -1.26777           0.00699  -181.4511     0.0000    -1.2815    -1.2541   \n",
      "  treatment*post_experiment    4.02026           0.02158   186.2831     0.0000     3.9780     4.0626   \n",
      "  treatment                    0.39971           0.01806    22.1322     0.0000     0.3643     0.4351   \n",
      "=======================================================================================================\n"
     ]
    }
   ],
   "source": [
    "# Select those matched observation ID from original panel df\n",
    "df_match_result = match_obj.df_out_final_post_trim\n",
    "matched_id_list = df_match_result['id'].values\n",
    "df_panel_post_match = df[df['id'].isin(matched_id_list)].copy()\n",
    "df_panel_post_match.reset_index(inplace=True, drop=True)\n",
    "\n",
    "formula = 'y ~ 0|0|0|0'\n",
    "\n",
    "\n",
    "# post match result\n",
    "model_did = did(data_df = df_panel_post_match,\n",
    "                formula = formula,\n",
    "                treatment = ['treatment'],\n",
    "                csid = ['id'],\n",
    "                tsid = ['time'],\n",
    "                exp_date = 'date_04')\n",
    "result = model_did.fit()\n",
    "result.summary()\n",
    "\n",
    "\n",
    "# pre match result\n",
    "model_did = did(data_df = df,\n",
    "                formula = formula,\n",
    "                treatment = ['treatment'],\n",
    "                csid = ['id'],\n",
    "                tsid = ['time'],\n",
    "                exp_date = 'date_04')\n",
    "result = model_did.fit()\n",
    "result.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c1cbf07",
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